پیوند ابزار الگوبرداری به یک سیستم مبتنی بر دانش برای توسعه عملکرد

In this work, a knowledge-based system for the benchmarking of public university libraries was developed in order to provide more useful information for decision-making and process benchmarking. This knowledge-based system converts numerical data into information that can be used to evaluate relative efficiency of public university libraries in Taiwan. We propose an integrated framework for the benchmarking wheel and knowledge-based system, including a database management subsystem, model base subsystem, knowledge acquisition subsystem, and dialogue subsystem. In the model-base, we use the DEA models, including the CCR and BCC models, in order to shed new light on the operational efficiency of public libraries in Taiwan. Finally, we provide strategies for improving inefficient public university libraries.

The goals of a public or academic library are to aid students and teachers, and to provide research for academic research. A library can accomplish these goals by providing functional services, such as data searching in library databases. In response to the rapid changes in information technology and to better meet the needs of users, more and more libraries are providing E-library services. Traditional library services offered to users are based on existing print collections. But to meet the needs of students and teachers, public university libraries need to improve their performance. Many tools and techniques could be used to improve the performance and effectiveness of public libraries, and benchmarking is a very versatile tool that can be applied in a variety of ways to meet a range of requirements for improvement. In recent years, many reports in the literature have proposed approaches that libraries or firms can use to improve their performance. One of these approaches is benchmarking, and several studies have focused on its theory and implementation, including those by Laeven and Smit, 2003, Ruthven and Magnay, 2002 and Favret, 2000, and Loessner (1999). Another approach is to use DEA models, and several have been proposed by Wang et al., 2008, Anderson et al., 2008, Shim, 2003, Kao and Liu, 2000, Chen, 1997a, Chen, 1997b and Chen, 1997c. A third approach is to adopt decision support systems or knowledge-based systems, several of which have been proposed by Gleeson and Ottensmann, 1994, Ottensmann and Gleeson, 1993, Dubey, 1984 and Chorba and Bommer, 1983. Shim (2003) applied an analytical technique called Data Envelopment Analysis (DEA) to calculate the relative technical efficiency of ninety-five academic research libraries that are members of the Association of Research Libraries. Kao and Liu (2000) adopted the concept of a membership function used in fuzzy set theory to represent imprecise data with missing data, and discussed an application to university libraries in Taiwan. Chen, 1997a, Chen, 1997b and Chen, 1997c employed a DEA model to evaluate the relative performance of 23 university and college libraries in Taipei. University libraries need to be productive so that they can provide better service under certain constraints and attract more readers. Given these circumstances, the DEA model is a mathematical model especially suitable for application to non-profit institutions like university libraries. Ottensmann and Gleeson (1993) developed a decision support system (DSS) to assist budgeting for acquiring public library materials and other areas of library management decision making. But no similar studies on benchmarking and DEA models for public university libraries have yet been done. Because of the complexity and importance of benchmarking for public university libraries, decision support systems or knowledge-based systems are frequently used as tools to support decision-making. Decision support systems are computer-based tools that aid managerial decision-making by presenting various effective alternatives. Since the 1990s, knowledge-based intelligent systems have been playing an important role as decision support tools. Thus, in this study, a knowledge-based system for the benchmarking of public university libraries was developed in order to provide more useful information for decision-making. A case study was carried out to verify the effectiveness of the system and illustrate its use. Our KBS can be used as a tool to improve the efficiency and performance of public university libraries. The rest of the paper is organized as follows: Section 2 briefly reviews the literature on benchmarking and knowledge-based systems. Section 3 describes the architecture of our knowledge-based system for process benchmarking. Section 4 presents the system implementation and results of empirical analysis. Finally, some concluding remarks and a summary are given in Section 5.

Benchmarking is a tool that provides goals for realistic process improvement and can help inefficient public university libraries to understand the changes required to improve their performance. An inefficient public university library can use benchmarking to identify and rectify problem areas, and implement strategic change initiatives. It can also help reduce operating costs and improve competitive advantage. This paper has reported the design and implementation of a knowledge-based system for benchmarking public university libraries. In this study, we used nonparametric DEA methods to analyze the overall, technical, and scale efficiency of public university libraries in Taiwan. The main findings can be summarized as follows. The CCR efficiency score analysis results show that three public university libraries are relatively efficient, and the BCC efficiency score analysis results show that four public university libraries are relatively efficient. All the inefficient public university libraries can improve their performance by increasing their outputs. Through the successful development of this prototype system (KBSPBI), it has been demonstrated that the most frequently referenced public university library is the benchmark public library. The National Taiwan University library was taken as a benchmark library for the other public university libraries in Taiwan. Finally, we have provided a strategy for improving inefficient public university libraries. Our strategy includes: (1) increasing user perception and (2) increasing public services. Although the KBSPBI system has been developed, there are still several ways in which we can further improve its functions. In particular, we can extend its functions to assist a province to evaluate its performance by using different models, such as Stochastic Frontier Analysis, the Fuzzy DEA Model, and the Malmquist index, to evaluate long term productivity. And there is a need to further develop the DEA models, which will more accurately reflect public libraries performance and identify those public libraries with the maximal return. The development of such models is the underlying component to the success of those models in public libraries performance evaluation.